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• The suggestions for addressing the four common methodological problems discussed in this analysis are improving the definition and measurement of policy effects of interest, reducing bias through more-careful consideration of confounding factors, ensuring that key model assumptions are met, and avoiding statistical models with low statistical power.
• The authors highlight evidence suggesting that many of the results from the best studies of gun policy were based on methods that reject the null hypothesis too frequently, had so little power that the published effects may not describe the true effect, and used methods likely to produce biased estimates of the policy effect.

Research on gun policy topics has often been controversial, partly because different researchers studying the same questions—and typically using the same or similar data sets—have often reported contradictory findings, which leads to confusion about the merits of the policy being studied. One potential explanation is that different researchers may be using methods that are more or less appropriate to the gun policy topics they are investigating.

In this report, part of the RAND Gun Policy in America initiative, the authors discuss four common methodological problems that they observed in the literature evaluating gun policies and offer suggestions for how future research on gun policies could be improved. In presenting these ideas, the authors hope to improve awareness of some of the weaknesses with commonly used methods for estimating gun policy effects, stimulate debate about how best to address some of these limitations, and encourage reviewers of research to advocate for stronger methods prior to accepting papers for publication.

• Chapter One

  Introduction

• Chapter Two

  Improving the Definition and Measurement of Policy Effects of Interest

• Chapter Three

  Reducing Bias Through More-Careful Consideration of Confounding Factors

• Chapter Four

  Ensuring That Key Model Assumptions Are Met

• Chapter Five

  Avoiding Statistical Models with Low Statistical Power

• Chapter Six

  Conclusions

• Appendix

  Details on Spline and Hybrid Models